May 22, 2024
The authors have presented a review of current survivalmethods and contemporary methods that could help handle some of the issues that arise with current methods. The authors then went through description of current methods. They discussedcensoring and while they bring some issues like non-informative censoring and truncation and even missing data, they don’t offer any solutions. The authors then brought up an overview ofsurvival functions, hazard functions, Kaplan-Meier estimator for survival and also comparing these curves. In their discussion of the log-rank test, they failed to mention it also requires an assumption of proportionality of hazard
curves and even went so far as to say that there are no specific criteria to be
satisfied for the correct application of the LR test but then later on do
define an instance breakdowns with proportionality so the test cannot be used.
The authors then go into an extended description of the Cox proportional hazard regression and the issues around proportionality. They described a number of potential causesof violation of this assumption. They then extended to a non-proportional hazard (NPH) regression model that incorporates a time-varying covariate and/or a stratified Cox regression model. However, these are typical proposed ways to deal withnon-proportionality which works with one offender. They also then mentioned an extended Cox regressionmodel that incorporates several time-varying predictors.
The Yang-Prentice model was shown and they said it allowsfor Cox modeling and NPH. The model hasworked well for NPH issues like crossing curves but does not well handle
dichotomous covariates. They then discussed other models: the cure rate model,
mixture model, and conditional survival model, all of which still use versions
of the Cox model or other parametric models. Also, they discussed alternative methods for when the PH assumption and need to test a treatment effect like the weighted LR
tests, restricted mean survival time (RMST), and Max-Combo test. They did
discuss the RMST in the most detail as this has been a more novel technique for
avoiding typical Cox assumptions and parametric assumptions. Finally, they did
discuss the old techniques of using parametric survival distributions for
modeling, but of course, one has to have a good idea of how well their data
fits one of these distributional patterns. Also, they discussed landmarking
analysis as an alternative approach for time-dependent covariates as well as
competing risks, which is really more about competing events in survival
analysis. The very last modeling theydiscussed were frailty models as they are time-varying random effect models where the random effect has a multiplicative effect on risk and can incorporate heterogeneity amongst individuals. They concluded with that each technique should be weighed accordingly for proper use to lead to correct application and interpretation of results.
Written by,
Usha Govindarajulu, PhD
Keywords: survival analysis, Cox models, non-proportional hazards, log-rank test, frailty model,
RMST, Max-Combo test
References
Beis G, Iliopoulos A, and Papasotiriou I(2024) “An Overview of Introductory and Advanced Survival Analysis Methods in Clinical Applications: Where Have we Come so far?” Anticancer Research: 44(2): 471-487. DOI: 10.21873/anticanres.16835
